Importing pandas and plotly express

import pandas as pd
import plotly.express as px

df = pd.read_csv('Airbnb_Open_Data.csv')

C:\Users\DELL\anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3146: DtypeWarning: Columns (25) have mixed types.Specify dtype option on import or set low_memory=False.
  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,

df.columns

Index(['id', 'NAME', 'host id', 'host_identity_verified', 'host name',
       'neighbourhood group', 'neighbourhood', 'lat', 'long', 'country',
       'country code', 'instant_bookable', 'cancellation_policy', 'room type',
       'Construction year', 'price', 'service fee', 'minimum nights',
       'number of reviews', 'last review', 'reviews per month',
       'review rate number', 'calculated host listings count',
       'availability 365', 'house_rules', 'license'],
      dtype='object')

Dropping columns not needed/ not significant

df.drop(columns={'host id','host name','lat','long', 'country code','house_rules','license'}, inplace = True)

df.head(5)

	id	NAME	host_identity_verified	neighbourhood group	neighbourhood	country	instant_bookable	cancellation_policy	room type	Construction year	price	service fee	minimum nights	number of reviews	last review	reviews per month	review rate number	calculated host listings count	availability 365
0	1001254	Clean & quiet apt home by the park	unconfirmed	Brooklyn	Kensington	United States	False	strict	Private room	2020.0	$966	$193	10.0	9.0	10/19/2021	0.21	4.0	6.0	286.0
1	1002102	Skylit Midtown Castle	verified	Manhattan	Midtown	United States	False	moderate	Entire home/apt	2007.0	$142	$28	30.0	45.0	5/21/2022	0.38	4.0	2.0	228.0
2	1002403	THE VILLAGE OF HARLEM....NEW YORK !	NaN	Manhattan	Harlem	United States	True	flexible	Private room	2005.0	$620	$124	3.0	0.0	NaN	NaN	5.0	1.0	352.0
3	1002755	NaN	unconfirmed	Brooklyn	Clinton Hill	United States	True	moderate	Entire home/apt	2005.0	$368	$74	30.0	270.0	7/5/2019	4.64	4.0	1.0	322.0
4	1003689	Entire Apt: Spacious Studio/Loft by central park	verified	Manhattan	East Harlem	United States	False	moderate	Entire home/apt	2009.0	$204	$41	10.0	9.0	11/19/2018	0.10	3.0	1.0	289.0

Finding null values in each column

for i in df.columns:
    print(df[i].isnull().mean())

0.0
0.002436670922718545
0.002816791586662638
0.00028265382703535123
0.00015594693905398687
0.005185235723545064
0.001023401787541789
0.0007407479605064377
0.0
0.0020857903098470743
0.0024074308716459224
0.002660844647608651
0.00398639362956754
0.001783643115429975
0.15490404389906334
0.1547675903273911
0.0031774188832249827
0.0031091920973888632
0.004366514293511633

Filling null values with 'unknown' for string based columns

df.iloc[:,1:8] = df.iloc[:,1:8].fillna('Unknown')

Removing all rows where 'last review' is null

df = df[df['last review'].notna()]

df.head(3)

	id	NAME	host_identity_verified	neighbourhood group	neighbourhood	country	instant_bookable	cancellation_policy	room type	Construction year	price	service fee	minimum nights	number of reviews	last review	reviews per month	review rate number	calculated host listings count	availability 365
0	1001254	Clean & quiet apt home by the park	unconfirmed	Brooklyn	Kensington	United States	False	strict	Private room	2020.0	$966	$193	10.0	9.0	10/19/2021	0.21	4.0	6.0	286.0
1	1002102	Skylit Midtown Castle	verified	Manhattan	Midtown	United States	False	moderate	Entire home/apt	2007.0	$142	$28	30.0	45.0	5/21/2022	0.38	4.0	2.0	228.0
3	1002755	Unknown	unconfirmed	Brooklyn	Clinton Hill	United States	True	moderate	Entire home/apt	2005.0	$368	$74	30.0	270.0	7/5/2019	4.64	4.0	1.0	322.0

For the numeric columns, each null value in each column is filled with the mean of the column. For the column of 'price' and'service fee', first the '$',',' charachters are removed after which they are convered into float data types so to be used in the mean calculation

df.iloc[:,9:14]

	Construction year	price	service fee	minimum nights	number of reviews
0	2020.0	$966	$193	10.0	9.0
1	2007.0	$142	$28	30.0	45.0
3	2005.0	$368	$74	30.0	270.0
4	2009.0	$204	$41	10.0	9.0
5	2013.0	$577	$115	3.0	74.0
...	...	...	...	...	...
102588	2016.0	$618	$124	1.0	177.0
102591	2005.0	$1,139	$228	5.0	17.0
102593	2017.0	$1,099	$220	1.0	8.0
102595	2016.0	$837	$167	1.0	1.0
102597	2015.0	$546	$109	2.0	5.0

86706 rows × 5 columns

df['price'] = df['price'].str.replace('$', '')

df['price'] = df['price'].str.replace(',', '')

df['service fee'] = df['service fee'].str.replace('$', '')

df['service fee'] = df['service fee'].str.replace(',', '')

df['price'] = df['price'].astype('float')

df['service fee'] = df['service fee'].astype('float')

df.iloc[:,9:14] = df.iloc[:,9:14].fillna(df.iloc[:,9:14].mean())

For the reviews and avaliability columns, similar method is used to fill the null values with the mean of the column.

df.iloc[:,15:] = df.iloc[:,15:].fillna(df.iloc[:,15:].mean())

Each column is again checked for null values

for i in df.columns:
    print(df[i].isnull().mean())

0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

The following are some visulisations based on the data

nb = df["neighbourhood group"].value_counts() 
fig = px.bar(y = nb.values,
             x = nb.index,
             color = nb.index,
             color_discrete_sequence=px.colors.sequential.PuBuGn,
             text = nb.values,
             title = "Top Neighbourhood group",
            template = 'ggplot2')
fig.update_layout(
    xaxis_title = "Neighbourhood group",
    yaxis_title = "count")
fig.show()

host_ident = df["host_identity_verified"].value_counts()
fig = px.pie(
    values = host_ident.values,
    names = host_ident.index,
    color_discrete_sequence=px.colors.sequential.PuBu,
    title= 'Host identity distribution',template = 'ggplot2'
)

fig.show()

fig = px.box(
    x=df["host_identity_verified"],
    y=df['price'],
    template= 'ggplot2', 
    title = 'Host identity confirmation comparing to the price')
fig.update_layout(
    xaxis_title = "Host identity",
    yaxis_title = "price",) 
fig.show()

rm = df["room type"].value_counts() 
fig = px.bar(y = rm.values,
             x = rm.index,
             color = rm.index,
             color_discrete_sequence=px.colors.sequential.PuBuGn,
             text = rm.values,
             title = "Room type",
             template = "ggplot2"
             )
fig.update_layout(
    xaxis_title = "Room type",
    yaxis_title = "count")
fig.show()

fig = px.box(
    x=df["room type"],
    y=df['price'],
    template= 'ggplot2', 
    title = 'Room type compared to the price')
fig.update_layout(
    xaxis_title = "Room type",
    yaxis_title = "price") 
fig.show()

fig = px.box(
    x=df["cancellation_policy"],
    y=df['price'],
    template= 'ggplot2', 
    title = 'cancellation policy compared to the price')
fig.update_layout(
    xaxis_title = "concellation policy",
    yaxis_title = "price") 
fig.show()